Solving Dynamic TSP by Using River Formation Dynamics

Rabanal, Pablo; Rodrı́guez, Ismael; Rubio, Fernando

doi:10.1109/icnc.2008.760

Cited by 35 publications

(19 citation statements)

References 4 publications

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“…The formation of tributaries and meanders are the results of this path. The RFD is presented in Rabanal et al 20 based on this idea. To apply the previous plan, ants and pheromone are replaced by drops and altitude.…”

Section: River Formation Dynamicsmentioning

confidence: 99%

Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Mehrjoo

Khunjush

2017

Computational Intelligence

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The restricted energy of nodes is one of the most important challenges in wireless sensor networks. Since data transmissions among nodes consume most of the nodes' energy, thus, minimizing the unnecessary transmissions reduces the consumed energy. One of the sources of this problem is the redundancy of raw data that can be eliminated at the aggregation points. As a result, data aggregation can be considered as an effective strategy to tackle the mentioned issue and to optimize the communication energy consumption. In this paper, the sensor nodes are organized in a tree structure, and the data aggregation are done in intermediate nodes at the junction of tree branches. One of the main characteristics of tree protocols is reduction of energy consumption through optimizing the structure of a data aggregation tree. For this, this paper proposes to apply a swarm intelligent algorithm named river formation dynamics. The simulation results show that the proposed algorithm outperforms in comparison to the famous ant colony optimization algorithm in terms of network lifetime. Simulations show that the proposed algorithm makes nearly 4% and 50% improvement in lifetime of wireless sensor networks than ant colony optimization and shortest path routing, respectively.

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Section: River Formation Dynamicsmentioning

confidence: 99%

Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Mehrjoo

Khunjush

2017

Computational Intelligence

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“…The water transforms the environment by eroding the ground when it falls through a high decreasing slope, and it deposits carried sediments when a flatter ground is reached. Basic algorithm and expressions for river formation dynamics are shown in [16,18,19]. From the beginningof the execution of RFD, any path from the origin point to the target pointhas a gradient that, considering this path as a whole (i.e.…”

Section: Natural Heuristic Approach: Location Area Management Usingmentioning

confidence: 99%

Advanced River Formation Dynamics for Location Area Management in GSM

Dholakiya¹,

Doshi²,

Ghiya³

et al. 2015

IJERT

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To handle call delivery for mobile station, itsprobable location should be known to the network. For that location management is required. Location Management has two basic operations: location update and paging.The goal of an efficient location management scheme is to maintain and provide locations of mobile stations at low cost. This paper presents location area based approach proposed by researchers and nature inspired approach proposed by us, to reduce location management cost.However, there is trade off between reduction of location update cost and paging. Simulation Results of a basic location area scheme and nature inspired technique(river formation dynamics) is presented.

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“…Mathematical model for the RFD has been elaborated in [31], [32], [81]. This algorithm begins by depositing all drops in the initial node.…”

Section: Initialize Drops 2 Initialize Nodes 3 While (Termination Cmentioning

confidence: 99%

“…Adapting RFD to TSP is given in [81], [82]. A TSP is modeled as a graph having each city as a node and a path between two cities as an arc (or edge) between two nodes.…”

Section:  Adapting Symmetric Rfd To Tspmentioning

confidence: 99%

Investigating Performance of Various Natural Computing Algorithms

Chawda¹,

Patel²

2017

IJISA

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Abstract-Nature is there since millenniums. Natural elements have withstood harsh complexities since years and have proved their efficiency in tackling them. This aspect has inspired many researchers to design algorithms based on phenomena in the natural world since the last couple of decades. Such algorithms are known as natural computing algorithms or nature inspired algorithms. These algorithms have established their ability to solve a large number of real-world complex problems by providing optimal solutions within the reasonable time duration. This paper presents an investigation by assessing the performance of some of the well-known natural computing algorithms with their variations. These algorithms include Genetic Algorithms, Ant Colony Optimization, River Formation Dynamics, Firefly Algorithm and Cuckoo Search. The Traveling Salesman Problem is used here as a test bed problem for performance evaluation of these algorithms. It is a kind of combinatorial optimization problem and known as one the most famous NP-Hard problems. It is simple and easy to understand, but at the same time, very difficult to find the optimal solution in a reasonable time -particularly with the increase in a number of cities. The source code for the above natural computing algorithms is developed in MATLAB R2015b and applied on several TSP instances given in TSPLIB library. Results obtained are analyzed based on various criteria such as tour length, required iterations, convergence time and quality of solutions. Conclusions derived from this analysis help to establish the superiority of Firefly Algorithms over the other algorithms in comparative terms.

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Solving Dynamic TSP by Using River Formation Dynamics

Cited by 35 publications

References 4 publications

Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Optimal data aggregation tree in wireless sensor networks based on improved river formation dynamics

Advanced River Formation Dynamics for Location Area Management in GSM

Investigating Performance of Various Natural Computing Algorithms

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